Feasibility of Integrating Urinary Proteomics and Machine Learning for Diagnosing Diabetic Nephropathy

Diabetic nephropathy (DN) represents the predominant microvascular complication associated with diabetes mellitus; however, existing diagnostic techniques are inadequate. This study evaluated candidate urinary protein biomarkers for diagnosing DN. A cohort comprising 59 patients with type 2 diabetes, 60 patients with DN, and 60 healthy volunteers was recruited. Urine proteomics was utilized to investigate differential protein expression levels among various patient groups and to identify potential biomarkers in conjunction with data analysis from the gene expression omnibus database. Machine learning classification methods were utilized to construct differential diagnosis models for DN. The data set IPX0003092000 was used to validate these diagnostic models. Six potential biomarkers─SERPINF1, FABP4, CP, CFB, C4A, and A1BG─were identified. The diagnostic models for DN, constructed by using machine learning algorithms, demonstrated robust diagnostic performance. Notably, models employing the glmnet, plr, and ranger classification methods achieved AUC values exceeding 0.800 in both the training and test data sets. In the validation cohort, the AUC values for models constructed using the ranger, glmnet, and plr methods were 0.928, 0.942, and 0.850, respectively. We evaluated six candidate urinary biomarkers (SERPINF1, FABP4, CP, CFB, C4A, and A1BG) using urinary proteomics and developed a diagnostic model for DN using machine learning algorithms.